1. Effect of Neighboring Flight Patterns on a Particular Flight Presented by Venugopal Rajagopal CIS 595 Dr. Slobodan Vucetic

2. Introduction  Lockheed Martin Dataset  3 months of flight data.  Tables used in the Database schema are Flight, Plan, PlanPoint, Tracking  About 3GB of data

3. Proposed Work  Develop a Matlab display routine for: a) Visualizing flight distribution in a particular airport for a particular radius. b) Visualizing the path a particular flight takes before landing or taking off from a particular airport.

4. Proposed Work (contd.)  Design a general model to predict delay using neighboring flight information.  Design a model to predict delay for different airlines.  Design a model to predict the difference in delta arrival and delta departure.

5. Data Preprocessing  Remove data which has null value.  Removing redundant data.  Data Interpolation.  Choosing airport hubs for data analysis.

6. Methodology  Neural Networks trained to design predictors.  Important attributes considered are a) Number of flights in air before departure of a particular flight b)Historical data (e.g., Average delay in departure one week before) c) Day of the week

7. Distribution of flights at a specific time Figure showing distribution of flights in a airport Figure showing flight path

8. Distribution of flights for the whole day Figure showing distribution of flights

9. Results  Classifier Accuracy General model to predict delay – 70% Separate model to predict delay for different airlines: American Airlines - 71% United Airlines - 71% North West - 71%

10. Conclusion  An attempt made to predict delay using neighboring flight information.  Different class projects done on this dataset, it will be good if bagging or boosting techniques are used to create ensembles to increase the prediction accuracies.